Identification pattern systems, such as ten print or fingerprint identification systems, have played a critical role in modern society in both criminal and civil applications. For example, criminal identification in public safety sectors is an integral part of any present day investigation. Similarly in civil applications such as credit card or personal identity fraud, print identification has become an essential part of the security process.
The automatic fingerprint identification operation normally consists of two stages. The first is the registration or enrollment stage and the second is the identification, authentication or verification stage. In the enrollment stage, an enrollee's prints and personal information are enrolled and matching features of the enrolled prints are extracted. Such matching features may include, but are not limited to, a classification of each print as one of four major print types (i.e., arch, left loop, right loop, and whorl) as well as minutiae which have respective X-Y coordinate positions and angles of orientation as is well known in the art.
The personal information and the extracted features, and perhaps the images, are then typically used to form a file record that is saved into a file database for subsequent identification of the enrollee. Present day automatic fingerprint identification systems may contain several hundred thousand to several million of such file records. In addition, one or more prints from an individual or a latent print may be obtained and matching features extracted from each print and, along with personal information, formed into what is typically referred to as a search record.
The search and file records may be processed, for instance, from unsolved latent prints, ten prints, a set of finger prints, slap prints, palm prints, etc. An unsolved latent print is typically defined as a finger or palm print lifted from a crime scene or unknown source whose owner is unknown. A ten-print or set of fingerprints generally means a set of 10 or less fingerprints that may have been taken from a person by either the rolled or flat method of capturing the prints in a media. A slap print is the literal slapping of the user's inked hands onto a media or the literal slapping of the user's hands onto a fingerprint capture sensor. Typically, the prints are processed to produce an active search record against which filtered file records processed from a file database is compared. It is understood that ten prints may include toe or palm prints as well.
In the identification stage, the search record is compared with the enrolled file records in the database of the fingerprint matching system. In a typical search scenario, a search record may be compared against millions of file records that are stored in the database and a list of matched scores is typically generated as a result of this matching process. Candidate records are sorted according to matched scores. A matched score (also referred to herein as a similarity score) is a measurement of the similarity of the print features of the identified search and file records. Typically, the higher the score, the more similar the file and search records are determined to be. Thus, a top candidate is the one that has the closest match.
More specifically, a known fingerprint identification system conceptually performs two stages of matching and three stages of decision-making. The two stages of matching are the minutiae matching stage (have two integrated stages or processes of registration and scoring) and the enhanced matching stage (wherein more detailed matching and scoring is performed). The three stages of decision-making are the minutiae matcher intelligent decision logic (e.g., IDL1), the enhanced matcher intelligent decision logic (e.g., IDL2), and a hybrid final intelligent decision logic (e.g., IDL3).
For ten-print matching, for instance, for a given pre-determined number of assigned fingers, the minutiae matcher in its registration stage aligns and compares a fingerprint in the search record to the corresponding fingerprint of a file record to find a set of mated minutiae for the compared prints. In the minutiae matching scoring stage, a score is calculated based on the mated minutiae for that compared fingerprint pair. For all the compared file records of the compared fingers, a list of scores is created to form a match report. The sorted match reports for each compared finger are generated based on the same given number of assigned fingers, which are then sent to IDL1 to make a first stage decision.
If no candidate file record (also referred to herein as a candidate record and a candidate) is detected as a hit or a candidate record is detected as a weak hit, a more detailed secondary matching using more orthogonal information, i.e., the enhanced matching, is conducted on the top N mated candidate records (wherein N may be for instance 100 or 200) to boost a mated candidate score. The same pre-determined number of assigned fingers is also used for this secondary matching. If no candidate is detected as a hit or a candidate is detected as a weak hit, the top “L” candidates are sent to the IDL3 stage, which combines both the minutiae match and detailed match results to make the final decision.
One shortcoming of the above system is that the same number of fingers is used in the registration and scoring stages of minutiae matching. Another shortcoming is that the same number of candidate file records is used in the registration and scoring stages of minutiae matching. It is well known that the greater the number of fingers used in matching, the more accurate the system. However due to speed and cost considerations, the number of fingers used in the matching is normally limited, for example to four to six, and the rest of fingers in the ten-print record are not used in the matching even though in theory they may provide extra orthogonal information to improve accuracy.
Thus, there is an ever-existing need for improving the speed and reducing the cost of fingerprint identification systems without sacrificing the accuracy of the system.